Feeding the embedding sequence \(Z_{embed}\)  of currently schedulable
nodes to the self-attention layer is achieved as follows: First, perform
a linear projection on \(Z_{embed}\) , that is, assign three weight
matrices \(W_{Q}\) ,\(W_{K}\) ,\(W_{V}\)\(\in \)\(\mathbb {R}^{embed.dim*embed.dim}\) 
to \(Z_{embed}\)  to obtain three matrices \(Q\) ,\(K\) ,\(V\)  representing
queries, keys, and values respectively, with the same dimensions.
Then the similarity is calculated by \(\sqrt{d_{k}}\) -regulated dot-product
to obtain the weights (\(QK^{T}/\sqrt{d_{k}}\) ), where \(d_{k}\)  is
the dimension of the key vectors serving as a scaling factor [1]},
and the softmax function is applied to normalize these weights; Finally,
the weights and corresponding value (matrix) \(V\)  are multiplied to
obtain the attention. This process can be done via matrix multiplications:
\(& Q=Linear(Z_{embed})=Z_{embed}W_{Q}\\& K=Linear(Z_{embed})=Z_{embed}W_{K}\\& V=Linear(Z_{embed})=Z_{embed}W_{V}\) 
\(Attention(Q,K,V)=\operatorname{soft}\max \left(\frac{QK^{T}}{\sqrt{d_{k}}}\right)V.\) 
